Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Mohammad Alzorgan, Joshua Carroll, Essam Al-Masalmeh, Abdel Mayyas

Research output: Contribution to journalArticle

Abstract

Advanced Driver Assistance Systems (ADAS) is an essential aspect of the automotive technology in this era of technological revolution, where the goal is to make vehicles more convenient, safe, and energy efficient. Taking advantage of more degrees of freedom available within vehicle "energy management" allows more margin to maximize efficiency in the propulsion systems. It is envisioned by this research that future fuel economy regulations will consider the potential benefits of emerging connectivity and automation technologies of vehicle's fuel consumption. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevation are obtained by use of Geographic Information System (GIS) maps in order to optimize the controller. The optimization is then reflected on the powertrain of the vehicle. The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade. This control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the propulsion system. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The paper presents multiple control strategies designed, implemented, and tested using real world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes.

Original languageEnglish (US)
JournalSAE Technical Papers
Volume2016-Octobeer
DOIs
StatePublished - 2016

Fingerprint

Hybrid vehicles
Advanced driver assistance systems
Model predictive control
Energy management
Dynamic programming
Propulsion
Energy management systems
Electric drives
Logic programming
Powertrains
Fuel economy
Internal combustion engines
Fuel consumption
Geographic information systems
Energy conservation
Energy utilization
Automation
Controllers
Processing

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

Cite this

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles. / Alzorgan, Mohammad; Carroll, Joshua; Al-Masalmeh, Essam; Mayyas, Abdel.

In: SAE Technical Papers, Vol. 2016-Octobeer, 2016.

Research output: Contribution to journalArticle

Alzorgan, Mohammad ; Carroll, Joshua ; Al-Masalmeh, Essam ; Mayyas, Abdel. / Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles. In: SAE Technical Papers. 2016 ; Vol. 2016-Octobeer.
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